# Scalable Deep Reinforcement Learning for Ride-Hailing

arXiv: https://arxiv.org/abs/2009.14679 · HTML: https://mlsanity.com/paper/2009.14679/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2020.

## Built on by (most cited first, 12 of 12)

- [Reinforcement Learning for Ridesharing: An Extended Survey](https://mlsanity.com/paper/2105.01099/) (2021)
- [Hindsight Learning for MDPs with Exogenous Inputs](https://mlsanity.com/paper/2207.06272/) (2022)
- [Learning and Information in Stochastic Networks and Queues](https://mlsanity.com/paper/2105.08769/) (2021)
- [Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey](https://mlsanity.com/paper/2108.04462/) (2021)
- [Exploiting Exogenous Structure for Sample-Efficient Reinforcement Learning](https://mlsanity.com/paper/2409.14557/) (2024)
- [Robo-taxi Fleet Coordination at Scale via Reinforcement Learning](https://mlsanity.com/paper/2504.06125/) (2025)
- [Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning](https://mlsanity.com/paper/2302.14833/) (2023)
- [Atomic Proximal Policy Optimization for Electric Robo-Taxi Dispatch and Charger Allocation](https://mlsanity.com/paper/2502.13392/) (2025)
- [Processing Network Controls via Deep Reinforcement Learning](https://mlsanity.com/paper/2205.02119/) (2022)
- [Towards More Efficient Shared Autonomous Mobility: A Learning-Based Fleet Repositioning Approach](https://mlsanity.com/paper/2210.08659/) (2022)
- [Deep-Dispatch: A Deep Reinforcement Learning-Based Vehicle Dispatch Algorithm for Advanced Air Mobility](https://mlsanity.com/paper/2312.10809/) (2023)
- [Is Pure Exploitation Sufficient in Exogenous MDPs with Linear Function Approximation?](https://mlsanity.com/paper/2601.20694/) (2026)

## Abstract (arXiv)

Ride-hailing services, such as Didi Chuxing, Lyft, and Uber, arrange thousands of cars to meet ride requests throughout the day. We consider a Markov decision process (MDP) model of a ride-hailing service system, framing it as a reinforcement learning (RL) problem. The simultaneous control of many agents (cars) presents a challenge for the MDP optimization because the action space grows exponentially with the number of cars. We propose a special decomposition for the MDP actions by sequentially assigning tasks to the drivers. The new actions structure resolves the scalability problem and enables the use of deep RL algorithms for control policy optimization. We demonstrate the benefit of our proposed decomposition with a numerical experiment based on real data from Didi Chuxing.